This notebook is the result of issue #43

Sum of forecasts or forecast the sum?

Decisions are seldom based on forecasts for single sensors. Therefore combining either sensor data or the forecasts of these timeseries is nescesarry to obtain forecasts for the quantitiy of interest. Various strategies can be employed to achieve this with each of them having advantages and disadvantages.

Here, we compare these strategies to forecast the total load on a substation:

1. Forecast the total load directly.
2. Forecast and combine:
   1. Total load large customers
   2. Residual load substation
3. Forecast and combine
   1. Individual load large customers
   2. Residual load substation

We are going to run this comparison for Westwoud, since there the customer population is most diverse. To generate forecasts, we use the openSTEF backtest pipeline. Because of the stochastic nature of the backtest we repeat it 10 times to aquire some statistics. We compare the resulting forecasts in terms of MAE, rMAE and rMAE for the lowest 5% of the values.

Imports and data preparation

Import packages

2023-07-27 13:22:31 [info     ] Proloaf not available, setting constructor to None

EMS measurements

Load, pre-process, and visualize

Check the validity of the measurements

Measurements large customers (C-ARM data)

Load, pre-process, and visualize

Let's inspect the measurements of large customers and determine their contribution to the total load

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\plotly\express\_core.py:1222: PerformanceWarning:

DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`

Change of plans

We find large differences between the total load, and the Customer_total. Let's change our original plan and asses: Investigate difference in accuracy:

• Forecast customer_total directly
• Forecast each customer seperately
• Forecast the largest 5% customers and combine the rest

Predictors

Load, pre-process, and visualize

	clearSky_dlf	clearSky_ulf	clouds	humidity	mxlD	pressure	radiation	snowDepth	temp	winddeg	...	E3A_A	E3A_I	E3B_A	E3B_I	E3C_A	E3C_I	E3D_A	E3D_I	E4A_A	E4A_I
2022-01-01 00:00:00+00:00	4.197540	6.352890	93.116760	0.965576	684.004272	102078.382812	1.000000e-10	0.0	8.168640	199.768753	...	0.000056	9.500000e-07	0.000056	9.500000e-07	0.000056	9.500000e-07	0.000056	9.500000e-07	0.000079	9.500000e-07
2022-01-01 00:15:00+00:00	4.226732	6.580745	93.916321	0.961707	699.542831	102072.406250	1.000000e-10	0.0	8.233459	203.644638	...	0.000058	8.400000e-07	0.000058	8.400000e-07	0.000058	8.400000e-07	0.000058	8.400000e-07	0.000079	8.400000e-07
2022-01-01 00:30:00+00:00	4.255924	6.808600	94.715881	0.957838	715.081390	102066.429688	1.000000e-10	0.0	8.298279	207.520523	...	0.000058	7.400000e-07	0.000058	7.400000e-07	0.000058	7.400000e-07	0.000058	7.400000e-07	0.000079	7.400000e-07
2022-01-01 00:45:00+00:00	4.285115	7.036455	95.515442	0.953969	730.619949	102060.453125	1.000000e-10	0.0	8.363098	211.396408	...	0.000058	8.000000e-07	0.000058	8.000000e-07	0.000058	8.000000e-07	0.000058	8.000000e-07	0.000079	8.000000e-07
2022-01-01 01:00:00+00:00	4.314307	7.264310	96.315002	0.950100	746.158508	102054.476562	1.000000e-10	0.0	8.427917	215.272293	...	0.000059	1.070000e-06	0.000059	1.070000e-06	0.000059	1.070000e-06	0.000059	1.070000e-06	0.000079	1.070000e-06

5 rows × 43 columns

Backtests

Configure training, prediction, and backtest specifications

Forecast Total Customers Directly

Perform and save the results of the backtest n_iterations times

2023-07-31 11:21:19 [info     ] Postproces in preparation of storing
2023-07-31 11:21:25 [info     ] Postproces in preparation of storing
2023-07-31 11:21:31 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

Each customer seperately

  0%|          | 0/136 [00:00<?, ?it/s]

2023-07-31 11:59:49 [info     ] Found 1820 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.05222681359044996 num_values=1820 pj_id=1
2023-07-31 11:59:49 [info     ] Removed 1820 NaN values        num_removed_values=1820
2023-07-31 11:59:58 [info     ] Postproces in preparation of storing
2023-07-31 12:00:03 [info     ] Postproces in preparation of storing
2023-07-31 12:00:10 [info     ] Postproces in preparation of storing
2023-07-31 12:00:11 [info     ] Found 828 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.023760330578512397 num_values=828 pj_id=1
2023-07-31 12:00:11 [info     ] Removed 828 NaN values         num_removed_values=828

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:00:20 [info     ] Postproces in preparation of storing
2023-07-31 12:00:25 [info     ] Postproces in preparation of storing
2023-07-31 12:00:30 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:00:43 [info     ] Postproces in preparation of storing
2023-07-31 12:00:48 [info     ] Postproces in preparation of storing
2023-07-31 12:00:53 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:01:04 [info     ] Postproces in preparation of storing
2023-07-31 12:01:12 [info     ] Postproces in preparation of storing
2023-07-31 12:01:19 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:01:29 [info     ] Postproces in preparation of storing
2023-07-31 12:01:35 [info     ] Postproces in preparation of storing
2023-07-31 12:01:40 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:01:52 [info     ] Postproces in preparation of storing
2023-07-31 12:02:00 [info     ] Postproces in preparation of storing
2023-07-31 12:02:07 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:02:24 [info     ] Postproces in preparation of storing
2023-07-31 12:02:34 [info     ] Postproces in preparation of storing
2023-07-31 12:02:41 [info     ] Postproces in preparation of storing
2023-07-31 12:02:42 [info     ] Found 90 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0025826446280991736 num_values=90 pj_id=1
2023-07-31 12:02:42 [info     ] Removed 90 NaN values          num_removed_values=90

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:02:51 [info     ] Postproces in preparation of storing
2023-07-31 12:02:56 [info     ] Postproces in preparation of storing
2023-07-31 12:03:00 [info     ] Postproces in preparation of storing
2023-07-31 12:03:01 [info     ] Found 288 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.008264462809917356 num_values=288 pj_id=1
2023-07-31 12:03:01 [info     ] Removed 288 NaN values         num_removed_values=288

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:03:10 [info     ] Postproces in preparation of storing
2023-07-31 12:03:15 [info     ] Postproces in preparation of storing
2023-07-31 12:03:24 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:03:33 [info     ] Postproces in preparation of storing
2023-07-31 12:03:39 [info     ] Postproces in preparation of storing
2023-07-31 12:03:43 [info     ] Postproces in preparation of storing
2023-07-31 12:03:44 [info     ] Found 274 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.007862718089990818 num_values=274 pj_id=1
2023-07-31 12:03:44 [info     ] Removed 274 NaN values         num_removed_values=274

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:03:56 [info     ] Postproces in preparation of storing
2023-07-31 12:04:02 [info     ] Postproces in preparation of storing
2023-07-31 12:04:10 [info     ] Postproces in preparation of storing
2023-07-31 12:04:11 [info     ] Found 171 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.00490702479338843 num_values=171 pj_id=1
2023-07-31 12:04:11 [info     ] Removed 171 NaN values         num_removed_values=171

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:04:21 [info     ] Postproces in preparation of storing
2023-07-31 12:04:29 [info     ] Postproces in preparation of storing
2023-07-31 12:04:34 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:04:43 [info     ] Postproces in preparation of storing
2023-07-31 12:04:49 [info     ] Postproces in preparation of storing
2023-07-31 12:04:53 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:05:01 [info     ] Postproces in preparation of storing
2023-07-31 12:05:07 [info     ] Postproces in preparation of storing
2023-07-31 12:05:11 [info     ] Postproces in preparation of storing
2023-07-31 12:05:12 [info     ] Found 30 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0008608815426997245 num_values=30 pj_id=1
2023-07-31 12:05:12 [info     ] Removed 30 NaN values          num_removed_values=30

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:05:19 [info     ] Postproces in preparation of storing
2023-07-31 12:05:24 [info     ] Postproces in preparation of storing
2023-07-31 12:05:30 [info     ] Postproces in preparation of storing
2023-07-31 12:05:31 [info     ] Found 2 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=5.73921028466483e-05 num_values=2 pj_id=1
2023-07-31 12:05:31 [info     ] Removed 2 NaN values           num_removed_values=2

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:05:42 [info     ] Postproces in preparation of storing
2023-07-31 12:05:46 [info     ] Postproces in preparation of storing
2023-07-31 12:05:51 [info     ] Postproces in preparation of storing
2023-07-31 12:05:52 [info     ] Found 85 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.002439164370982553 num_values=85 pj_id=1
2023-07-31 12:05:52 [info     ] Removed 85 NaN values          num_removed_values=85

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:06:01 [info     ] Postproces in preparation of storing
2023-07-31 12:06:06 [info     ] Postproces in preparation of storing
2023-07-31 12:06:11 [info     ] Postproces in preparation of storing
2023-07-31 12:06:12 [info     ] Found 8618 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.24730257116620752 num_values=8618 pj_id=1
2023-07-31 12:06:12 [info     ] Removed 8618 NaN values        num_removed_values=8618

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:06:18 [info     ] Postproces in preparation of storing
2023-07-31 12:06:24 [info     ] Postproces in preparation of storing
2023-07-31 12:06:30 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:06:39 [info     ] Postproces in preparation of storing
2023-07-31 12:06:45 [info     ] Postproces in preparation of storing
2023-07-31 12:06:50 [info     ] Postproces in preparation of storing
2023-07-31 12:06:51 [info     ] Found 14 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0004017447199265381 num_values=14 pj_id=1
2023-07-31 12:06:51 [info     ] Removed 14 NaN values          num_removed_values=14

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:06:59 [info     ] Postproces in preparation of storing
2023-07-31 12:07:05 [info     ] Postproces in preparation of storing
2023-07-31 12:07:10 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:07:20 [info     ] Postproces in preparation of storing
2023-07-31 12:07:25 [info     ] Postproces in preparation of storing
2023-07-31 12:07:30 [info     ] Postproces in preparation of storing
2023-07-31 12:07:31 [info     ] Found 126 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.003615702479338843 num_values=126 pj_id=1
2023-07-31 12:07:31 [info     ] Removed 126 NaN values         num_removed_values=126

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:07:41 [info     ] Postproces in preparation of storing
2023-07-31 12:07:46 [info     ] Postproces in preparation of storing
2023-07-31 12:07:51 [info     ] Postproces in preparation of storing
2023-07-31 12:07:52 [info     ] Found 325 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.009326216712580349 num_values=325 pj_id=1
2023-07-31 12:07:52 [info     ] Removed 325 NaN values         num_removed_values=325

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:08:01 [info     ] Postproces in preparation of storing
2023-07-31 12:08:06 [info     ] Postproces in preparation of storing
2023-07-31 12:08:13 [info     ] Postproces in preparation of storing
2023-07-31 12:08:14 [info     ] Found 11512 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.3303489439853076 num_values=11512 pj_id=1
2023-07-31 12:08:14 [info     ] Removed 11512 NaN values       num_removed_values=11512

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:08:23 [info     ] Postproces in preparation of storing
2023-07-31 12:08:29 [info     ] Postproces in preparation of storing
2023-07-31 12:08:35 [info     ] Postproces in preparation of storing
2023-07-31 12:08:36 [info     ] Found 220 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.006313131313131313 num_values=220 pj_id=1

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:08:36 [info     ] Removed 220 NaN values         num_removed_values=220
2023-07-31 12:08:48 [info     ] Postproces in preparation of storing
2023-07-31 12:08:53 [info     ] Postproces in preparation of storing
2023-07-31 12:08:59 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:09:08 [info     ] Postproces in preparation of storing
2023-07-31 12:09:12 [info     ] Postproces in preparation of storing
2023-07-31 12:09:16 [info     ] Postproces in preparation of storing
2023-07-31 12:09:17 [info     ] Found 540 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.015495867768595042 num_values=540 pj_id=1
2023-07-31 12:09:17 [info     ] Removed 540 NaN values         num_removed_values=540

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:09:27 [info     ] Postproces in preparation of storing
2023-07-31 12:09:33 [info     ] Postproces in preparation of storing
2023-07-31 12:09:40 [info     ] Postproces in preparation of storing
2023-07-31 12:09:42 [info     ] Found 11 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0003156565656565657 num_values=11 pj_id=1

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:09:42 [info     ] Removed 11 NaN values          num_removed_values=11
2023-07-31 12:09:53 [info     ] Postproces in preparation of storing
2023-07-31 12:09:57 [info     ] Postproces in preparation of storing
2023-07-31 12:10:02 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:10:13 [info     ] Postproces in preparation of storing
2023-07-31 12:10:19 [info     ] Postproces in preparation of storing
2023-07-31 12:10:25 [info     ] Postproces in preparation of storing
2023-07-31 12:10:25 [info     ] Found 6 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0001721763085399449 num_values=6 pj_id=1
2023-07-31 12:10:25 [info     ] Removed 6 NaN values           num_removed_values=6

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:10:35 [info     ] Postproces in preparation of storing
2023-07-31 12:10:41 [info     ] Postproces in preparation of storing
2023-07-31 12:10:47 [info     ] Postproces in preparation of storing
2023-07-31 12:10:48 [info     ] Found 131 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0037591827364554637 num_values=131 pj_id=1
2023-07-31 12:10:48 [info     ] Removed 131 NaN values         num_removed_values=131

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:10:57 [info     ] Postproces in preparation of storing
2023-07-31 12:11:07 [info     ] Postproces in preparation of storing
2023-07-31 12:11:14 [info     ] Postproces in preparation of storing
2023-07-31 12:11:15 [info     ] Found 14 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0004017447199265381 num_values=14 pj_id=1
2023-07-31 12:11:15 [info     ] Removed 14 NaN values          num_removed_values=14

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:11:26 [info     ] Postproces in preparation of storing
2023-07-31 12:11:33 [info     ] Postproces in preparation of storing
2023-07-31 12:11:39 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:11:54 [info     ] Postproces in preparation of storing
2023-07-31 12:12:02 [info     ] Postproces in preparation of storing
2023-07-31 12:12:09 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:12:19 [info     ] Postproces in preparation of storing
2023-07-31 12:12:24 [info     ] Postproces in preparation of storing
2023-07-31 12:12:29 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:12:38 [info     ] Postproces in preparation of storing
2023-07-31 12:12:44 [info     ] Postproces in preparation of storing
2023-07-31 12:12:50 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:13:00 [info     ] Postproces in preparation of storing
2023-07-31 12:13:06 [info     ] Postproces in preparation of storing
2023-07-31 12:13:10 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:13:21 [info     ] Postproces in preparation of storing
2023-07-31 12:13:26 [info     ] Postproces in preparation of storing
2023-07-31 12:13:31 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:13:42 [info     ] Postproces in preparation of storing
2023-07-31 12:13:51 [info     ] Postproces in preparation of storing
2023-07-31 12:13:57 [info     ] Postproces in preparation of storing
2023-07-31 12:13:58 [info     ] Found 49 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0014061065197428833 num_values=49 pj_id=1
2023-07-31 12:13:58 [info     ] Removed 49 NaN values          num_removed_values=49

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:14:08 [info     ] Postproces in preparation of storing
2023-07-31 12:14:14 [info     ] Postproces in preparation of storing
2023-07-31 12:14:22 [info     ] Postproces in preparation of storing
2023-07-31 12:14:23 [info     ] Found 115 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.003300045913682277 num_values=115 pj_id=1
2023-07-31 12:14:23 [info     ] Removed 115 NaN values         num_removed_values=115

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:14:31 [info     ] Postproces in preparation of storing
2023-07-31 12:14:36 [info     ] Postproces in preparation of storing
2023-07-31 12:14:40 [info     ] Postproces in preparation of storing
2023-07-31 12:14:41 [info     ] Found 131 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0037591827364554637 num_values=131 pj_id=1
2023-07-31 12:14:41 [info     ] Removed 131 NaN values         num_removed_values=131

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:14:50 [info     ] Postproces in preparation of storing
2023-07-31 12:14:55 [info     ] Postproces in preparation of storing
2023-07-31 12:15:01 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:15:11 [info     ] Postproces in preparation of storing
2023-07-31 12:15:16 [info     ] Postproces in preparation of storing
2023-07-31 12:15:19 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:15:31 [info     ] Postproces in preparation of storing
2023-07-31 12:15:37 [info     ] Postproces in preparation of storing
2023-07-31 12:15:43 [info     ] Postproces in preparation of storing
2023-07-31 12:15:44 [info     ] Found 100 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.002869605142332415 num_values=100 pj_id=1
2023-07-31 12:15:44 [info     ] Removed 100 NaN values         num_removed_values=100

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:15:55 [info     ] Postproces in preparation of storing
2023-07-31 12:16:01 [info     ] Postproces in preparation of storing
2023-07-31 12:16:07 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:16:17 [info     ] Postproces in preparation of storing
2023-07-31 12:16:23 [info     ] Postproces in preparation of storing
2023-07-31 12:16:29 [info     ] Postproces in preparation of storing
2023-07-31 12:16:31 [info     ] Found 11 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0003156565656565657 num_values=11 pj_id=1
2023-07-31 12:16:31 [info     ] Removed 11 NaN values          num_removed_values=11

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:16:42 [info     ] Postproces in preparation of storing
2023-07-31 12:16:47 [info     ] Postproces in preparation of storing
2023-07-31 12:16:51 [info     ] Postproces in preparation of storing
2023-07-31 12:16:52 [info     ] Found 3 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=8.608815426997245e-05 num_values=3 pj_id=1
2023-07-31 12:16:52 [info     ] Removed 3 NaN values           num_removed_values=3

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:17:00 [info     ] Postproces in preparation of storing
2023-07-31 12:17:04 [info     ] Postproces in preparation of storing
2023-07-31 12:17:09 [info     ] Postproces in preparation of storing
2023-07-31 12:17:10 [info     ] Found 6 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0001721763085399449 num_values=6 pj_id=1
2023-07-31 12:17:10 [info     ] Removed 6 NaN values           num_removed_values=6

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:17:22 [info     ] Postproces in preparation of storing
2023-07-31 12:17:28 [info     ] Postproces in preparation of storing
2023-07-31 12:17:37 [info     ] Postproces in preparation of storing
2023-07-31 12:17:38 [info     ] Found 54 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0015495867768595042 num_values=54 pj_id=1
2023-07-31 12:17:38 [info     ] Removed 54 NaN values          num_removed_values=54

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:17:47 [info     ] Postproces in preparation of storing
2023-07-31 12:17:53 [info     ] Postproces in preparation of storing
2023-07-31 12:17:58 [info     ] Postproces in preparation of storing
2023-07-31 12:17:59 [info     ] Found 28 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0008034894398530762 num_values=28 pj_id=1
2023-07-31 12:17:59 [info     ] Removed 28 NaN values          num_removed_values=28

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:18:14 [info     ] Postproces in preparation of storing
2023-07-31 12:18:20 [info     ] Postproces in preparation of storing
2023-07-31 12:18:25 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:18:34 [info     ] Postproces in preparation of storing
2023-07-31 12:18:40 [info     ] Postproces in preparation of storing
2023-07-31 12:18:46 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:18:57 [info     ] Postproces in preparation of storing
2023-07-31 12:19:06 [info     ] Postproces in preparation of storing
2023-07-31 12:19:12 [info     ] Postproces in preparation of storing
2023-07-31 12:19:13 [info     ] Found 21 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0006026170798898072 num_values=21 pj_id=1
2023-07-31 12:19:13 [info     ] Removed 21 NaN values          num_removed_values=21

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:19:22 [info     ] Postproces in preparation of storing
2023-07-31 12:19:28 [info     ] Postproces in preparation of storing
2023-07-31 12:19:34 [info     ] Postproces in preparation of storing
2023-07-31 12:19:35 [info     ] Found 97 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0027835169880624424 num_values=97 pj_id=1
2023-07-31 12:19:35 [info     ] Removed 97 NaN values          num_removed_values=97

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:19:44 [info     ] Postproces in preparation of storing
2023-07-31 12:19:50 [info     ] Postproces in preparation of storing
2023-07-31 12:19:55 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:20:08 [info     ] Postproces in preparation of storing
2023-07-31 12:20:13 [info     ] Postproces in preparation of storing
2023-07-31 12:20:18 [info     ] Postproces in preparation of storing
2023-07-31 12:20:19 [info     ] Found 10 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0002869605142332415 num_values=10 pj_id=1
2023-07-31 12:20:19 [info     ] Removed 10 NaN values          num_removed_values=10

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:20:26 [info     ] Postproces in preparation of storing
2023-07-31 12:20:33 [info     ] Postproces in preparation of storing
2023-07-31 12:20:40 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:20:42 [info     ] Found 387 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.011105371900826446 num_values=387 pj_id=1
2023-07-31 12:20:42 [info     ] Removed 387 NaN values         num_removed_values=387
2023-07-31 12:21:10 [info     ] Postproces in preparation of storing
2023-07-31 12:21:24 [info     ] Postproces in preparation of storing
2023-07-31 12:21:32 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:21:45 [info     ] Postproces in preparation of storing
2023-07-31 12:21:51 [info     ] Postproces in preparation of storing
2023-07-31 12:21:56 [info     ] Postproces in preparation of storing
2023-07-31 12:21:57 [info     ] Found 72 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.002066115702479339 num_values=72 pj_id=1
2023-07-31 12:21:57 [info     ] Removed 72 NaN values          num_removed_values=72

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:22:11 [info     ] Postproces in preparation of storing
2023-07-31 12:22:18 [info     ] Postproces in preparation of storing
2023-07-31 12:22:23 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:22:34 [info     ] Postproces in preparation of storing
2023-07-31 12:22:40 [info     ] Postproces in preparation of storing
2023-07-31 12:22:45 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:22:54 [info     ] Postproces in preparation of storing
2023-07-31 12:23:01 [info     ] Postproces in preparation of storing
2023-07-31 12:23:08 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:23:20 [info     ] Postproces in preparation of storing
2023-07-31 12:23:26 [info     ] Postproces in preparation of storing
2023-07-31 12:23:31 [info     ] Postproces in preparation of storing
2023-07-31 12:23:32 [info     ] Found 12 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0003443526170798898 num_values=12 pj_id=1
2023-07-31 12:23:32 [info     ] Removed 12 NaN values          num_removed_values=12

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:23:41 [info     ] Postproces in preparation of storing
2023-07-31 12:23:48 [info     ] Postproces in preparation of storing
2023-07-31 12:23:55 [info     ] Postproces in preparation of storing
2023-07-31 12:23:56 [info     ] Found 23 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0006600091827364554 num_values=23 pj_id=1
2023-07-31 12:23:56 [info     ] Removed 23 NaN values          num_removed_values=23

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:24:04 [info     ] Postproces in preparation of storing
2023-07-31 12:24:09 [info     ] Postproces in preparation of storing
2023-07-31 12:24:13 [info     ] Postproces in preparation of storing
2023-07-31 12:24:14 [info     ] Found 57 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0016356749311294766 num_values=57 pj_id=1
2023-07-31 12:24:14 [info     ] Removed 57 NaN values          num_removed_values=57

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:24:22 [info     ] Postproces in preparation of storing
2023-07-31 12:24:27 [info     ] Postproces in preparation of storing
2023-07-31 12:24:32 [info     ] Postproces in preparation of storing
2023-07-31 12:24:33 [info     ] Found 224 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.006427915518824609 num_values=224 pj_id=1
2023-07-31 12:24:33 [info     ] Removed 224 NaN values         num_removed_values=224

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:24:41 [info     ] Postproces in preparation of storing
2023-07-31 12:24:46 [info     ] Postproces in preparation of storing
2023-07-31 12:24:53 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:25:03 [info     ] Postproces in preparation of storing
2023-07-31 12:25:10 [info     ] Postproces in preparation of storing
2023-07-31 12:25:15 [info     ] Postproces in preparation of storing
2023-07-31 12:25:16 [info     ] Found 11 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0003156565656565657 num_values=11 pj_id=1
2023-07-31 12:25:16 [info     ] Removed 11 NaN values          num_removed_values=11

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:25:25 [info     ] Postproces in preparation of storing
2023-07-31 12:25:30 [info     ] Postproces in preparation of storing
2023-07-31 12:25:35 [info     ] Postproces in preparation of storing
2023-07-31 12:25:36 [info     ] Found 12 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0003443526170798898 num_values=12 pj_id=1
2023-07-31 12:25:36 [info     ] Removed 12 NaN values          num_removed_values=12

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:25:45 [info     ] Postproces in preparation of storing
2023-07-31 12:25:51 [info     ] Postproces in preparation of storing
2023-07-31 12:25:55 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:26:05 [info     ] Postproces in preparation of storing
2023-07-31 12:26:10 [info     ] Postproces in preparation of storing
2023-07-31 12:26:15 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:26:26 [info     ] Postproces in preparation of storing
2023-07-31 12:26:34 [info     ] Postproces in preparation of storing
2023-07-31 12:26:40 [info     ] Postproces in preparation of storing
2023-07-31 12:26:41 [info     ] Found 10 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0002869605142332415 num_values=10 pj_id=1
2023-07-31 12:26:41 [info     ] Removed 10 NaN values          num_removed_values=10

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:26:49 [info     ] Postproces in preparation of storing
2023-07-31 12:26:55 [info     ] Postproces in preparation of storing
2023-07-31 12:27:00 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:27:09 [info     ] Postproces in preparation of storing
2023-07-31 12:27:15 [info     ] Postproces in preparation of storing
2023-07-31 12:27:20 [info     ] Postproces in preparation of storing
2023-07-31 12:27:21 [info     ] Found 578 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.01658631772268136 num_values=578 pj_id=1
2023-07-31 12:27:21 [info     ] Removed 578 NaN values         num_removed_values=578

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:27:29 [info     ] Postproces in preparation of storing
2023-07-31 12:27:34 [info     ] Postproces in preparation of storing
2023-07-31 12:27:40 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:27:49 [info     ] Postproces in preparation of storing
2023-07-31 12:27:57 [info     ] Postproces in preparation of storing
2023-07-31 12:28:03 [info     ] Postproces in preparation of storing
2023-07-31 12:28:04 [info     ] Found 28 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0008034894398530762 num_values=28 pj_id=1
2023-07-31 12:28:04 [info     ] Removed 28 NaN values          num_removed_values=28

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:28:13 [info     ] Postproces in preparation of storing
2023-07-31 12:28:18 [info     ] Postproces in preparation of storing
2023-07-31 12:28:24 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:28:34 [info     ] Postproces in preparation of storing
2023-07-31 12:28:38 [info     ] Postproces in preparation of storing
2023-07-31 12:28:43 [info     ] Postproces in preparation of storing
2023-07-31 12:28:43 [info     ] Found 3416 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0980257116620753 num_values=3416 pj_id=1
2023-07-31 12:28:43 [info     ] Removed 3416 NaN values        num_removed_values=3416

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:28:55 [info     ] Postproces in preparation of storing
2023-07-31 12:29:00 [info     ] Postproces in preparation of storing
2023-07-31 12:29:04 [info     ] Postproces in preparation of storing
2023-07-31 12:29:05 [info     ] Found 8399 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.24101813590449955 num_values=8399 pj_id=1
2023-07-31 12:29:05 [info     ] Removed 8399 NaN values        num_removed_values=8399

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:29:13 [info     ] Postproces in preparation of storing
2023-07-31 12:29:17 [info     ] Postproces in preparation of storing
2023-07-31 12:29:22 [info     ] Postproces in preparation of storing
2023-07-31 12:29:22 [info     ] Found 18 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0005165289256198347 num_values=18 pj_id=1
2023-07-31 12:29:22 [info     ] Removed 18 NaN values          num_removed_values=18

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:29:31 [info     ] Postproces in preparation of storing
2023-07-31 12:29:39 [info     ] Postproces in preparation of storing
2023-07-31 12:29:46 [info     ] Postproces in preparation of storing
2023-07-31 12:29:47 [info     ] Found 12 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0003443526170798898 num_values=12 pj_id=1
2023-07-31 12:29:47 [info     ] Removed 12 NaN values          num_removed_values=12

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:29:57 [info     ] Postproces in preparation of storing
2023-07-31 12:30:01 [info     ] Postproces in preparation of storing
2023-07-31 12:30:07 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:30:18 [info     ] Postproces in preparation of storing
2023-07-31 12:30:24 [info     ] Postproces in preparation of storing
2023-07-31 12:30:29 [info     ] Postproces in preparation of storing
2023-07-31 12:30:30 [info     ] Found 54 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0015495867768595042 num_values=54 pj_id=1
2023-07-31 12:30:30 [info     ] Removed 54 NaN values          num_removed_values=54

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:30:39 [info     ] Postproces in preparation of storing
2023-07-31 12:30:44 [info     ] Postproces in preparation of storing
2023-07-31 12:30:50 [info     ] Postproces in preparation of storing
2023-07-31 12:30:51 [info     ] Found 9 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.00025826446280991736 num_values=9 pj_id=1
2023-07-31 12:30:51 [info     ] Removed 9 NaN values           num_removed_values=9

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:31:00 [info     ] Postproces in preparation of storing
2023-07-31 12:31:07 [info     ] Postproces in preparation of storing
2023-07-31 12:31:13 [info     ] Postproces in preparation of storing
2023-07-31 12:31:14 [info     ] Found 38 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0010904499540863178 num_values=38 pj_id=1
2023-07-31 12:31:14 [info     ] Removed 38 NaN values          num_removed_values=38

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:31:24 [info     ] Postproces in preparation of storing
2023-07-31 12:31:30 [info     ] Postproces in preparation of storing
2023-07-31 12:31:36 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:31:45 [info     ] Postproces in preparation of storing
2023-07-31 12:31:53 [info     ] Postproces in preparation of storing
2023-07-31 12:32:00 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:32:15 [info     ] Postproces in preparation of storing
2023-07-31 12:32:23 [info     ] Postproces in preparation of storing
2023-07-31 12:32:28 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:32:39 [info     ] Postproces in preparation of storing
2023-07-31 12:32:47 [info     ] Postproces in preparation of storing
2023-07-31 12:32:53 [info     ] Postproces in preparation of storing
2023-07-31 12:32:54 [info     ] Found 6867 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.19705578512396693 num_values=6867 pj_id=1
2023-07-31 12:32:54 [info     ] Removed 6867 NaN values        num_removed_values=6867

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:33:01 [info     ] Postproces in preparation of storing
2023-07-31 12:33:05 [info     ] Postproces in preparation of storing
2023-07-31 12:33:09 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:33:20 [info     ] Postproces in preparation of storing
2023-07-31 12:33:26 [info     ] Postproces in preparation of storing
2023-07-31 12:33:31 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:33:40 [info     ] Postproces in preparation of storing
2023-07-31 12:33:46 [info     ] Postproces in preparation of storing
2023-07-31 12:33:51 [info     ] Postproces in preparation of storing
2023-07-31 12:33:52 [info     ] Found 58 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0016643709825528007 num_values=58 pj_id=1
2023-07-31 12:33:52 [info     ] Removed 58 NaN values          num_removed_values=58

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:34:00 [info     ] Postproces in preparation of storing
2023-07-31 12:34:05 [info     ] Postproces in preparation of storing
2023-07-31 12:34:10 [info     ] Postproces in preparation of storing
2023-07-31 12:34:11 [info     ] Found 180 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.005165289256198347 num_values=180 pj_id=1
2023-07-31 12:34:11 [info     ] Removed 180 NaN values         num_removed_values=180

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:34:20 [info     ] Postproces in preparation of storing
2023-07-31 12:34:25 [info     ] Postproces in preparation of storing
2023-07-31 12:34:32 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:34:44 [info     ] Postproces in preparation of storing
2023-07-31 12:34:51 [info     ] Postproces in preparation of storing
2023-07-31 12:34:56 [info     ] Postproces in preparation of storing
2023-07-31 12:34:56 [info     ] Found 204 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.005853994490358127 num_values=204 pj_id=1
2023-07-31 12:34:56 [info     ] Removed 204 NaN values         num_removed_values=204

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:35:06 [info     ] Postproces in preparation of storing
2023-07-31 12:35:11 [info     ] Postproces in preparation of storing
2023-07-31 12:35:17 [info     ] Postproces in preparation of storing
2023-07-31 12:35:18 [info     ] Found 3 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=8.608815426997245e-05 num_values=3 pj_id=1
2023-07-31 12:35:18 [info     ] Removed 3 NaN values           num_removed_values=3

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:35:27 [info     ] Postproces in preparation of storing
2023-07-31 12:35:32 [info     ] Postproces in preparation of storing
2023-07-31 12:35:37 [info     ] Postproces in preparation of storing
2023-07-31 12:35:38 [info     ] Found 18 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0005165289256198347 num_values=18 pj_id=1
2023-07-31 12:35:38 [info     ] Removed 18 NaN values          num_removed_values=18

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:35:48 [info     ] Postproces in preparation of storing
2023-07-31 12:35:54 [info     ] Postproces in preparation of storing
2023-07-31 12:36:06 [info     ] Postproces in preparation of storing
2023-07-31 12:36:07 [info     ] Found 11 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0003156565656565657 num_values=11 pj_id=1
2023-07-31 12:36:07 [info     ] Removed 11 NaN values          num_removed_values=11

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:36:16 [info     ] Postproces in preparation of storing
2023-07-31 12:36:24 [info     ] Postproces in preparation of storing
2023-07-31 12:36:31 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:36:40 [info     ] Postproces in preparation of storing
2023-07-31 12:36:47 [info     ] Postproces in preparation of storing
2023-07-31 12:36:53 [info     ] Postproces in preparation of storing
2023-07-31 12:36:54 [info     ] Found 11 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0003156565656565657 num_values=11 pj_id=1
2023-07-31 12:36:54 [info     ] Removed 11 NaN values          num_removed_values=11

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:37:03 [info     ] Postproces in preparation of storing
2023-07-31 12:37:11 [info     ] Postproces in preparation of storing
2023-07-31 12:37:17 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:37:27 [info     ] Postproces in preparation of storing
2023-07-31 12:37:33 [info     ] Postproces in preparation of storing
2023-07-31 12:37:41 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:37:54 [info     ] Postproces in preparation of storing
2023-07-31 12:38:00 [info     ] Postproces in preparation of storing
2023-07-31 12:38:06 [info     ] Postproces in preparation of storing
2023-07-31 12:38:07 [info     ] Found 8 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0002295684113865932 num_values=8 pj_id=1
2023-07-31 12:38:07 [info     ] Removed 8 NaN values           num_removed_values=8

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:38:15 [info     ] Postproces in preparation of storing
2023-07-31 12:38:21 [info     ] Postproces in preparation of storing
2023-07-31 12:38:25 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:38:35 [info     ] Postproces in preparation of storing
2023-07-31 12:38:40 [info     ] Postproces in preparation of storing
2023-07-31 12:38:45 [info     ] Postproces in preparation of storing
2023-07-31 12:38:46 [info     ] Found 249 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.007145316804407714 num_values=249 pj_id=1
2023-07-31 12:38:46 [info     ] Removed 249 NaN values         num_removed_values=249

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:38:54 [info     ] Postproces in preparation of storing
2023-07-31 12:39:00 [info     ] Postproces in preparation of storing
2023-07-31 12:39:06 [info     ] Postproces in preparation of storing
2023-07-31 12:39:07 [info     ] Found 24 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0006887052341597796 num_values=24 pj_id=1
2023-07-31 12:39:07 [info     ] Removed 24 NaN values          num_removed_values=24

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:39:16 [info     ] Postproces in preparation of storing
2023-07-31 12:39:22 [info     ] Postproces in preparation of storing
2023-07-31 12:39:28 [info     ] Postproces in preparation of storing
2023-07-31 12:39:29 [info     ] Found 11 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0003156565656565657 num_values=11 pj_id=1
2023-07-31 12:39:29 [info     ] Removed 11 NaN values          num_removed_values=11

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:39:39 [info     ] Postproces in preparation of storing
2023-07-31 12:39:47 [info     ] Postproces in preparation of storing
2023-07-31 12:39:54 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:40:04 [info     ] Postproces in preparation of storing
2023-07-31 12:40:10 [info     ] Postproces in preparation of storing
2023-07-31 12:40:15 [info     ] Postproces in preparation of storing
2023-07-31 12:40:16 [info     ] Found 5 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.00014348025711662075 num_values=5 pj_id=1
2023-07-31 12:40:16 [info     ] Removed 5 NaN values           num_removed_values=5

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:40:24 [info     ] Postproces in preparation of storing
2023-07-31 12:40:29 [info     ] Postproces in preparation of storing
2023-07-31 12:40:35 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:40:44 [info     ] Postproces in preparation of storing
2023-07-31 12:40:48 [info     ] Postproces in preparation of storing
2023-07-31 12:40:53 [info     ] Postproces in preparation of storing
2023-07-31 12:40:54 [info     ] Found 1507 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.043244949494949496 num_values=1507 pj_id=1
2023-07-31 12:40:54 [info     ] Removed 1507 NaN values        num_removed_values=1507

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:41:00 [info     ] Postproces in preparation of storing
2023-07-31 12:41:04 [info     ] Postproces in preparation of storing
2023-07-31 12:41:08 [info     ] Postproces in preparation of storing
2023-07-31 12:41:09 [info     ] Found 3 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=8.608815426997245e-05 num_values=3 pj_id=1
2023-07-31 12:41:09 [info     ] Removed 3 NaN values           num_removed_values=3

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:41:16 [info     ] Postproces in preparation of storing
2023-07-31 12:41:22 [info     ] Postproces in preparation of storing
2023-07-31 12:41:27 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:41:36 [info     ] Postproces in preparation of storing
2023-07-31 12:41:41 [info     ] Postproces in preparation of storing
2023-07-31 12:41:45 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:41:53 [info     ] Postproces in preparation of storing
2023-07-31 12:41:57 [info     ] Postproces in preparation of storing
2023-07-31 12:42:01 [info     ] Postproces in preparation of storing
2023-07-31 12:42:02 [info     ] Found 25 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0007174012855831037 num_values=25 pj_id=1
2023-07-31 12:42:02 [info     ] Removed 25 NaN values          num_removed_values=25

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:42:09 [info     ] Postproces in preparation of storing
2023-07-31 12:42:14 [info     ] Postproces in preparation of storing
2023-07-31 12:42:18 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:42:26 [info     ] Postproces in preparation of storing
2023-07-31 12:42:30 [info     ] Postproces in preparation of storing
2023-07-31 12:42:34 [info     ] Postproces in preparation of storing
2023-07-31 12:42:35 [info     ] Found 6 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0001721763085399449 num_values=6 pj_id=1
2023-07-31 12:42:35 [info     ] Removed 6 NaN values           num_removed_values=6

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:42:42 [info     ] Postproces in preparation of storing
2023-07-31 12:42:47 [info     ] Postproces in preparation of storing
2023-07-31 12:42:51 [info     ] Postproces in preparation of storing
2023-07-31 12:42:52 [info     ] Found 159 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.00456267217630854 num_values=159 pj_id=1
2023-07-31 12:42:52 [info     ] Removed 159 NaN values         num_removed_values=159

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:42:58 [info     ] Postproces in preparation of storing
2023-07-31 12:43:03 [info     ] Postproces in preparation of storing
2023-07-31 12:43:07 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:43:15 [info     ] Postproces in preparation of storing
2023-07-31 12:43:19 [info     ] Postproces in preparation of storing
2023-07-31 12:43:25 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:43:33 [info     ] Postproces in preparation of storing
2023-07-31 12:43:39 [info     ] Postproces in preparation of storing
2023-07-31 12:43:45 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:43:52 [info     ] Postproces in preparation of storing
2023-07-31 12:43:56 [info     ] Postproces in preparation of storing
2023-07-31 12:44:01 [info     ] Postproces in preparation of storing
2023-07-31 12:44:02 [info     ] Found 76 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.0021808999081726357 num_values=76 pj_id=1
2023-07-31 12:44:02 [info     ] Removed 76 NaN values          num_removed_values=76

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:44:08 [info     ] Postproces in preparation of storing
2023-07-31 12:44:12 [info     ] Postproces in preparation of storing
2023-07-31 12:44:17 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:44:26 [info     ] Postproces in preparation of storing
2023-07-31 12:44:30 [info     ] Postproces in preparation of storing
2023-07-31 12:44:36 [info     ] Postproces in preparation of storing
2023-07-31 12:44:37 [info     ] Found 142 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.004074839302112029 num_values=142 pj_id=1
2023-07-31 12:44:37 [info     ] Removed 142 NaN values         num_removed_values=142

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:44:44 [info     ] Postproces in preparation of storing
2023-07-31 12:44:48 [info     ] Postproces in preparation of storing
2023-07-31 12:44:53 [info     ] Postproces in preparation of storing
2023-07-31 12:44:53 [info     ] Found 1 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=2.869605142332415e-05 num_values=1 pj_id=1
2023-07-31 12:44:53 [info     ] Removed 1 NaN values           num_removed_values=1

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:45:00 [info     ] Postproces in preparation of storing
2023-07-31 12:45:05 [info     ] Postproces in preparation of storing
2023-07-31 12:45:09 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:45:17 [info     ] Postproces in preparation of storing
2023-07-31 12:45:23 [info     ] Postproces in preparation of storing
2023-07-31 12:45:28 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:45:36 [info     ] Postproces in preparation of storing
2023-07-31 12:45:41 [info     ] Postproces in preparation of storing
2023-07-31 12:45:46 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:45:53 [info     ] Postproces in preparation of storing
2023-07-31 12:45:57 [info     ] Postproces in preparation of storing
2023-07-31 12:46:02 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:46:10 [info     ] Postproces in preparation of storing
2023-07-31 12:46:14 [info     ] Postproces in preparation of storing
2023-07-31 12:46:20 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:46:29 [info     ] Postproces in preparation of storing
2023-07-31 12:46:34 [info     ] Postproces in preparation of storing
2023-07-31 12:46:39 [info     ] Postproces in preparation of storing
2023-07-31 12:46:40 [info     ] Found 192 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.005509641873278237 num_values=192 pj_id=1
2023-07-31 12:46:40 [info     ] Removed 192 NaN values         num_removed_values=192

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:46:47 [info     ] Postproces in preparation of storing
2023-07-31 12:46:51 [info     ] Postproces in preparation of storing
2023-07-31 12:46:54 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:47:02 [info     ] Postproces in preparation of storing
2023-07-31 12:47:06 [info     ] Postproces in preparation of storing
2023-07-31 12:47:11 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:47:19 [info     ] Postproces in preparation of storing
2023-07-31 12:47:24 [info     ] Postproces in preparation of storing
2023-07-31 12:47:33 [info     ] Postproces in preparation of storing
2023-07-31 12:47:34 [info     ] Found 20 values of constant load (repeated values), converted to NaN value. cleansing_step=repeated_values frac_values=0.000573921028466483 num_values=20 pj_id=1
2023-07-31 12:47:34 [info     ] Removed 20 NaN values          num_removed_values=20

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:47:42 [info     ] Postproces in preparation of storing
2023-07-31 12:47:47 [info     ] Postproces in preparation of storing
2023-07-31 12:47:52 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

2023-07-31 12:48:02 [info     ] Postproces in preparation of storing
2023-07-31 12:48:07 [info     ] Postproces in preparation of storing
2023-07-31 12:48:11 [info     ] Postproces in preparation of storing

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

feature_importance_dataframe is not saved in Scikit-Learn meta.

c:\Users\al11943\Anaconda3\envs\AIFES\lib\site-packages\xgboost\sklearn.py:761: UserWarning:

standard_deviation is not saved in Scikit-Learn meta.

Evaluate results

Index([], dtype='object')

  0%|          | 0/135 [00:00<?, ?it/s]

Export notebook as html

Write this notebook to html.

Command to be executed: jupyter nbconvert 43.Compare_SumForecastsCustomers_vs_ForecastSumCustomers.ipynb --to html --no-input --output results/43.Compare_SumForecastsCustomers_vs_ForecastSumCustomers.html.

Open points:

• What is the horizon of the customer power forecasts?
  • The power forecasts are updated about 20-35 times per day. For this analysis, the most recent power forecasts are used. So, we can assume the forecast horizon is about one hour.
• Why is the curtailment of the 25th of may not incorporated in the Vattenfall power forecasts?
  • This was a request from TenneT to the Alliander operations to switch of the fields of the Vattenfall windpark. There was no request to Vattenfall.
• How do the customer power forecasts compare to the OpenSTEF forecasts in terms of quality?
• Regarding the 19th of december incident: How long before curtailment, was the curtailment request sent by TenneT?
  • The curtailment request was sent in real-time / just in time.